Geopandas_Practice.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Geopandas Practice",
      "provenance": [],
      "authorship_tag": "ABX9TyOdyNq16ol32O9jtU6SY+nM"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "EOJQuySWjFz7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 469
        },
        "outputId": "ee118dd3-56d8-441b-b8a0-132f7931ca6c"
      },
      "source": [
        "pip install geopandas"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting geopandas\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/5b/0c/e6c99e561b03482220f00443f610ccf4dce9b50f4b1093d735f93c6fc8c6/geopandas-0.6.2-py2.py3-none-any.whl (919kB)\n",
            "\u001b[K     |████████████████████████████████| 921kB 2.8MB/s \n",
            "\u001b[?25hRequirement already satisfied: shapely in /usr/local/lib/python3.6/dist-packages (from geopandas) (1.6.4.post2)\n",
            "Collecting fiona\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/50/f7/9899f8a9a2e38601472fe1079ce5088f58833221c8b8507d8b5eafd5404a/Fiona-1.8.13-cp36-cp36m-manylinux1_x86_64.whl (11.8MB)\n",
            "\u001b[K     |████████████████████████████████| 11.8MB 15.0MB/s \n",
            "\u001b[?25hCollecting pyproj\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/d6/70/eedc98cd52b86de24a1589c762612a98bea26cde649ffdd60c1db396cce8/pyproj-2.4.2.post1-cp36-cp36m-manylinux2010_x86_64.whl (10.1MB)\n",
            "\u001b[K     |████████████████████████████████| 10.1MB 54.4MB/s \n",
            "\u001b[?25hRequirement already satisfied: pandas>=0.23.0 in /usr/local/lib/python3.6/dist-packages (from geopandas) (0.25.3)\n",
            "Requirement already satisfied: attrs>=17 in /usr/local/lib/python3.6/dist-packages (from fiona->geopandas) (19.3.0)\n",
            "Requirement already satisfied: six>=1.7 in /usr/local/lib/python3.6/dist-packages (from fiona->geopandas) (1.12.0)\n",
            "Collecting munch\n",
            "  Downloading https://files.pythonhosted.org/packages/cc/ab/85d8da5c9a45e072301beb37ad7f833cd344e04c817d97e0cc75681d248f/munch-2.5.0-py2.py3-none-any.whl\n",
            "Collecting click-plugins>=1.0\n",
            "  Downloading https://files.pythonhosted.org/packages/e9/da/824b92d9942f4e472702488857914bdd50f73021efea15b4cad9aca8ecef/click_plugins-1.1.1-py2.py3-none-any.whl\n",
            "Requirement already satisfied: click<8,>=4.0 in /usr/local/lib/python3.6/dist-packages (from fiona->geopandas) (7.0)\n",
            "Collecting cligj>=0.5\n",
            "  Downloading https://files.pythonhosted.org/packages/e4/be/30a58b4b0733850280d01f8bd132591b4668ed5c7046761098d665ac2174/cligj-0.5.0-py3-none-any.whl\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.23.0->geopandas) (2018.9)\n",
            "Requirement already satisfied: numpy>=1.13.3 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.23.0->geopandas) (1.17.5)\n",
            "Requirement already satisfied: python-dateutil>=2.6.1 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.23.0->geopandas) (2.6.1)\n",
            "Installing collected packages: munch, click-plugins, cligj, fiona, pyproj, geopandas\n",
            "Successfully installed click-plugins-1.1.1 cligj-0.5.0 fiona-1.8.13 geopandas-0.6.2 munch-2.5.0 pyproj-2.4.2.post1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AHzorB8ejKgA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import geopandas as gpd\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NEXKgdKGjZVX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 371
        },
        "outputId": "ca78817e-2f5d-43ce-8b30-bf8f08e16483"
      },
      "source": [
        "world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))\n",
        "world.head()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>pop_est</th>\n",
              "      <th>continent</th>\n",
              "      <th>name</th>\n",
              "      <th>iso_a3</th>\n",
              "      <th>gdp_md_est</th>\n",
              "      <th>geometry</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>920938</td>\n",
              "      <td>Oceania</td>\n",
              "      <td>Fiji</td>\n",
              "      <td>FJI</td>\n",
              "      <td>8374.0</td>\n",
              "      <td>MULTIPOLYGON (((180.00000 -16.06713, 180.00000...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>53950935</td>\n",
              "      <td>Africa</td>\n",
              "      <td>Tanzania</td>\n",
              "      <td>TZA</td>\n",
              "      <td>150600.0</td>\n",
              "      <td>POLYGON ((33.90371 -0.95000, 34.07262 -1.05982...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>603253</td>\n",
              "      <td>Africa</td>\n",
              "      <td>W. Sahara</td>\n",
              "      <td>ESH</td>\n",
              "      <td>906.5</td>\n",
              "      <td>POLYGON ((-8.66559 27.65643, -8.66512 27.58948...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>35623680</td>\n",
              "      <td>North America</td>\n",
              "      <td>Canada</td>\n",
              "      <td>CAN</td>\n",
              "      <td>1674000.0</td>\n",
              "      <td>MULTIPOLYGON (((-122.84000 49.00000, -122.9742...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>326625791</td>\n",
              "      <td>North America</td>\n",
              "      <td>United States of America</td>\n",
              "      <td>USA</td>\n",
              "      <td>18560000.0</td>\n",
              "      <td>MULTIPOLYGON (((-122.84000 49.00000, -120.0000...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     pop_est  ...                                           geometry\n",
              "0     920938  ...  MULTIPOLYGON (((180.00000 -16.06713, 180.00000...\n",
              "1   53950935  ...  POLYGON ((33.90371 -0.95000, 34.07262 -1.05982...\n",
              "2     603253  ...  POLYGON ((-8.66559 27.65643, -8.66512 27.58948...\n",
              "3   35623680  ...  MULTIPOLYGON (((-122.84000 49.00000, -122.9742...\n",
              "4  326625791  ...  MULTIPOLYGON (((-122.84000 49.00000, -120.0000...\n",
              "\n",
              "[5 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-0Y48v1Zjq9_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "outputId": "e26b1e4a-3d51-48c1-bb13-341baa01ef75"
      },
      "source": [
        "# Plot countries\n",
        "world.plot()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fee89134da0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
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7wzGeWtvEO7s6cIdi1HUHknHryiwz0bhEXJIAgUWlDgQgw6xjeWUmTouONKNmQssK93f6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\nEAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7EgKWql1jxWH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "167059e9-f277-4539-e96f-174f429d9d52"
      },
      "source": [
        "# Check which column is the active geometry column\n",
        "world.geometry.name"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'geometry'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UV3zXF8PkJEA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "b5a831b5-03f8-4816-886f-cdfc376ff5a9"
      },
      "source": [
        "# Rename the column to Borders & Set that to Geometry\n",
        "world = world.rename(columns={'geometry': 'borders'}).set_geometry('borders')\n",
        "world.geometry.name"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'borders'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IkiX27hYkgX1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "outputId": "42059441-6a00-4002-8e82-2eef4666c14d"
      },
      "source": [
        "# Create centroids and make it to the geometry using centroid attribute\n",
        "world['centroid_column'] = world.centroid\n",
        "world = world.set_geometry('centroid_column')\n",
        "world.plot()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fee863d5080>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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fCn81ERHJWtqW/B8Da939LQB3f7W+/Epgg7u/5e7PUps85JyU6xIRkYTSBvn3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\ngg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OcNgiFsUk41w",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        },
        "outputId": "568158c8-2676-4e38-8a1d-ae9c35e080f6"
      },
      "source": [
        "world['centroid_column'].head()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    POINT (163.85316 -17.31631)\n",
              "1      POINT (34.75299 -6.25773)\n",
              "2     POINT (-12.13783 24.29117)\n",
              "3     POINT (-98.14238 61.46908)\n",
              "4    POINT (-112.59944 45.70563)\n",
              "Name: centroid_column, dtype: geometry"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_ex-z7OflWYE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "outputId": "dbdd407f-db86-4cb3-d3de-0db1b1c3f023"
      },
      "source": [
        "# Slice souther hemisphere using coordinate-based indexing\n",
        "world = world.set_geometry('borders')\n",
        "southern_hemisphere = world.cx[:, :0]\n",
        "southern_hemisphere.plot(figsize=(10,10))"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fee85d73198>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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4DBP4G5ohW/z7tauydcQBaKrdPOg5aXYzRp3i3TIH6XYzC7PjyetRWNIXCGG3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\nSUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VjSWVpvCl68Y",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "2fcd8dc0-8912-4a5c-fd5e-6ec0c0075080"
      },
      "source": [
        "world.geometry.name"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'centroid_column'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-n1keZMVmQPF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "outputId": "ce474110-2dd4-4adc-ec99-bc6cb910a4ea"
      },
      "source": [
        "# Read in Cities data\n",
        "cities = gpd.read_file(gpd.datasets.get_path('naturalearth_cities'))\n",
        "cities.head()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>name</th>\n",
              "      <th>geometry</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Vatican City</td>\n",
              "      <td>POINT (12.45339 41.90328)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>San Marino</td>\n",
              "      <td>POINT (12.44177 43.93610)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Vaduz</td>\n",
              "      <td>POINT (9.51667 47.13372)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Luxembourg</td>\n",
              "      <td>POINT (6.13000 49.61166)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Palikir</td>\n",
              "      <td>POINT (158.14997 6.91664)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           name                   geometry\n",
              "0  Vatican City  POINT (12.45339 41.90328)\n",
              "1    San Marino  POINT (12.44177 43.93610)\n",
              "2         Vaduz   POINT (9.51667 47.13372)\n",
              "3    Luxembourg   POINT (6.13000 49.61166)\n",
              "4       Palikir  POINT (158.14997 6.91664)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EoiMumktnwYB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 165
        },
        "outputId": "f87cb0f9-0b6a-4e39-8435-733b65e4f504"
      },
      "source": [
        "cities.plot()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fee85f2cc50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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teB07oli8s7NHhl8RVBXlMNwfYUeteO0Qkgx7ZBdKTjFPYyefXIOt9y3vmP5/6ureLv+r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\nd5QqXgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nvYTIRUwn2CV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "outputId": "172146ec-98aa-4fb7-c1f2-cb739c23d696"
      },
      "source": [
        "# Create a chloropleth map by setting color with column=\n",
        "southern_hemisphere.plot(figsize=(10,10), column='gdp_md_est')"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fee85e72cf8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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6RHD9uHE7930wl0r7wUR4c34RN7z8Kf+cs6jF8+94aw6rcvKaXKqmuOrguD+v\nT1PtcPH5yo0EY6SAUoqFHy7t+ht3kmMuwdqbUxDsEMRRwmg0cMFVx/PCx7dw3DT/s29q7G0br+Pz\n+vjglWksnT+F5Ysn4XL6HzfRKYxJXXevI8ym5Ttw2tu31mRscmwrjonGoOCX79cRnxpD/6w+JPdJ\nrN/vcrgJjwqlrMD/DMbTrpnBgLGNK3yXFVbwzt8/4/oxd3P10NspzittsP/835/O0EkD2viMRFfw\n+nys2JHLa/NX4fEzdq+1tu4r5vJ/v89Vz3/IXj+rCsxb17ib2OH2sLOorN337IgrHriQtQva11p8\nQHV5DYs/XRagiNrvmEuwknoFcRCxOColpURz3z8v5p9vXMPoiQ3f5A4ftNwaHz6bx1+udvGXq8Fo\n7MKCpKaBXXevI0xlWQ0JqTEMGNWLuB5RbTrXYzIRmRDR5P5+ozKoLCynsrjuza84r4wtK3awd0dh\nffKzbXUOYdFh5G3d1+DclMwkbvjH5dzyzNV+r73ok2W8+qf32LFud6N9Sil6ZCQcM1PmjyTFlTXc\n9Mpn/LBhOyt2+C9s3FYrduRy4VP/5bm5S6m017VWV9odzN/of2D7GwtXBeS+bRWXHNOh5YCcdidv\nPfABsd2g6G6HyjQopR4DzgBcwHbgKq11+f59fwB+DXiBW7XW33Qw1oCQ9bpEZxk0vCd/fe4KPnh1\nIS8/NRelwFTZ9kHRB3g9Xv5+17n84R8fdH5zvYoAg7RgNSUhKYJ9a7ezDwiNCiWtVwI+r0Z7fWit\n0Vrj8/lIzEylIL8MR42L2moHRqOB0PAQ0nplUrG3FEe1g4Se8RTsLqF0XzkpmUlsX70DbxPjaNQh\nlbUL9xTXf53arwfXP345Y2eNbPY17auXf2hyX0xSFKNOHNr2b4bodJ+s2MCi7J38tGVXywe3QWmN\nnWe/XcKPG3dw3vihzN+Ug93t/0Pcx8vW89tTJhMVGhLQGFojJLR9i0E/e+sr1FbbuePlG7vFbOiO\n1sGaC/xBa+1RSv0d+APwf0qpwcBFwBAgBZinlOqvtW79aE8hjlDnXTmZtIx4PnrrJ6oK21aU8nAL\nP9vDNf+X3PlV30Mv7xYvSN1V76HpJPWKp2BXMbUVtexa6/+Nb9+2gy1MyX2SiEuJobaqmnW/bCVj\nSE/ytu5lz+Y8kvsk4aiuZceaHHQz3T9eP4s89x6azt++uofYHi0vnxIR13TB2MyRvVo8X3Q9l8fD\n8u11rVa+TppSvCG3gA25zQ+X8fo0Lk83X9LrML2G9CQkzNptXss61Jyjtf5Wa33gJ7AUSNv/9Wzg\nXa21U2udA2wDxnXkXkIcKfNUlToAACAASURBVJRSTJw2iIpKO7v2VdGjDSUdDpeUHk18YucvqaMs\nIzv9Hkey8OgwHvjkrjZ1+UYnRrJ+0WZ2rNmFs9ZF9vLt9XWwjEZDXetXC2NrKg8rCzHrqmk8ufCB\nViVXAGNnjmhy39TzJrbqGqJrPfHlooC3XLXXk3MWU17T+lnQwXb6dScx47IpwQ6jXiD7y64Gvtr/\ndSpw6Gqoufu3CXHMSEmLRfs0sf2brkFzgMlsZMiEfvTs14OQEAtms5HB4/oy7qSoLugeDAfT4E6+\nyZGv97B0xpzcdMJyuOY+Redu3cuQif1bvIYt4mD3zIV3nslvn78GW3jru2yGTfY/rs4SYmbSWX6X\nTxNdTGtNYUU1v+zM56+f/sBbi1YHO6R6n63cyEl/fZlXf1zRoF6WaJ0WuwiVUvMAf7Xz79Vaf7b/\nmHsBD/B2WwNQSl0LXAuQnt50jRchjjRh+98cDa6W60sNHJvJ+sPKAGxcupUppyc2cUbgqKhHUUaZ\n/NEas2+ayfKvA1OnZ8OSLQzIyqRgdzHlhRWN9ltCzBTnH5zJNeOyKW0eQ/r9O4v9bp9w2mjCImUd\nwmCxu9xU2p0szt7J12u2dJsWK3/sLjf/+HIhy3fk8u+rzwr49b9+5Xv2bM7jigcvwmI9ugodt/jX\nqrWeobUe6uffgeTqSuB04FJ9sNBGHnDox/a0/dv8Xf8FrXWW1jorIUFe5MXRI2R/faxyX8tviv6W\nUAHYtr59gz1bzXI8KmRG597jKJJ18og2rffXkuwV25tcC7B/ViZl+w6O4fvgH/9r07Vz1u3myxe/\n87tvyrnj23QtERhaa16fv5ITH3yR6Q+9yH0fzO3WydWhFmzKYc4vgV8zdeZV03DaXdw14y+U7A1O\naYjO0qEuQqXULOAu4Eytde0huz4HLlJKWZVSvYF+QPCLUgjRhWr2V8vOLagibUAKACkDkhkwbTDx\nPeMA6DU8ncSMBPZk+x9nZTDWFZfsrHpYyjqpU657tDIYDNz+4vWER4c1e9ygCf2anBl4uE1Lt9Jr\nUBp9RmZg3v8Jvv+YPmxcsqXBcT+8u7i+Antr/OvWV3E5GtfustosjJ0lY+66UnmNneXbc7n4X+/w\n2BcLqHIcmdXzn5u7NOAV3pVS3PT01cy8chp3Tv8LG5duafmkI0RHZxE+A1iBufvHGyzVWl+vtd6g\nlHof2Ehd1+FNMoNQHGvycw9+GovplYjJamLHwAQqPV4s49IZPbY3S/FgBkYVOaiav6nRNb59ew+L\nvzgBt9PDlNk9uP2RDwMWn4p6AmU7PWDXO1akD0rlhAuP44v/NL0GnL3awc71e5rcfyhziJnwAcls\n0ODrGQ+ALq5s1KrpdnlYu2Aj408d3arrNlW5e/T0YW0axyU67pPlG3j1xxWUHkEDxv3JKSxl+sMv\nctusSZw1dkjArquU4pRfT2fS2eOoKa9t+YQjRIcSLK11k4uxaa0fBh7uyPWFOJLl7zlYNXvd7gr6\njehFpbtuVphLa5ZSNwHXDSxPsDLhtJFs+rLx+J6airoX5WXzShvtaz0jhJyOsk4E83DACsaUDlzv\n2BYR23T5g7ZI6p2IMyuTFbUNx+kZ0uNhSeNP8vEpLVeFP2DSWWMbLItzQNbM4W0PVHSI1+c74pOr\nA4oqa/jvT78wO2twwMshRMZGEBnbdFHeI41U3RSiE1SW11J9yCLPoWFWHFFND+DUKJaEQO9LJjJg\n+hAMxsZ/mpUlNbz977P59tOZrQ/E2BvCbkLFfY4h+jGU7RyUqS/K1BOlpIJ3e4VFdXyAuFIKd1Zf\n9tY2ngSxxeGu7y48lMvZ+uV6Rk5t3MKglGL8KaPaFqjosPeXrgt2CAG1MbeQBz76DrdXOqaaIwmW\nEJ1gz86DVbcTk6Mp7xvGuuIWWqCUYrXLwc8xFiIvHkfmhL6NPiG+9Vg+qxa28hNe2DWo+P9hiLgN\nZQ7cwGwB/Ub3bna/LcLW7P7EjAQyLziOvFr/Y3FqPT4i4w/+nMefOoq/f/vHNg2wT+mbRERMw7Fi\nWTOHk5ge3+priI7TWpMY2fyYvSPRBz+v45ZXP8fZRCV40fExWEIIP3J3HUywTBYjtW18EdrpcLIz\nLYKMvuMpf6vh6vIDs3xoFIqmB5uq8NtQ4Te1LWjRagOyMjGZjXj8VFoHcPVOIiI7n6rSagD6Tx6I\nOTac3Ys243Z5iBrfn7XNDFi3GqAkr5SJZ4zhN3+7lJ4D2t6dGxph4/mVj/DZs9+Sv6OAc26dxaAJ\nLdfeEoGllOLWWZO4+j+BGz/ZXSzK3skjn//I/efKTGR/pAVLiE6wL//g9Hp/3X2tleRunES9uSGV\ntblnNH2SqR+E3djue4qWhUbYGNnMOn4On4+e+xes7TuxP5tjwlnh1RROHEDZlMHNJlcDwq1YvlpN\ncu9EZl19YruSqwMS0uL4zd8u5r73fsvQSQMxduB3UbSP1+fjy9WbSTgKW7EA5qzObnJCxbFO/tqE\n6ARlJdX1X5ut7WsoTrKY2fm/hlWd+00eQL7DRZWjmbINhthusxbX0Wz4lOar39tjwxl4wXGsiwnH\neejU9hZ+Njafxu10kzIukzJ7y0VqRfdmNBhwuD0UVdYEO5ROYXe5kfzKP+kiFKITFBdWAhAZHUph\npIKqFk7wo8jpJvOcMcS6vBjsHtzhFnaHO3jruhwy475t+kRf46rgIvBmXHo8bz34od9aUwAbq/a3\nUrUx2f2l1kXiBZNYXO3klimDOhqm6AbuOnMqX67eHOwwOoVSkFdWQc+41q2PeSyRFiwhAqxwbzmr\nlm4HIGZ4EnlV7fvk6lOw1eHgZ5+bJVaNbVAtH93zWfPJFYBZikh2hfjUWO5+8xb6TxpA+vCGy3x1\ntBZjocNNWFgIyYlRHbuQ6BbiwkM5rn+vYIfRKbw+zXtL1gY7jG5JWrCECLDvvlqL1+sjrU8Ca1qa\nOdhK50xxcccZ76Oobv5A83BU+M0Buado2eSzxrGx2sE3X6xiSGYy3ooaHIlRbKpqZcV1rRkZZsW7\nu4h9afEU7W8NC7VZuOfWU6Sr9yhy9+wTOOvxN/Adhf1pNsvRtYZgoEgLlhABdmANwojEwBSjnD3J\nzR1nvNNycmWIQ8W8hDJ2/gLR4qDLzp2A22pmhcvDapu19ckVMNpmZvO7i9j6UzbhprqX4/BQK88/\neinHB3DNQxF8fRJjGdMnNdhhdIqpg5ovW3KskgRLiAA7sAZhM1UUWi3CaCR0TQEF+0a0eKyKuBdl\nkHEQXS3UZuG6y6e069wSo4G0wWmkXnI8OdV1NbGuu3wKvXtKraqjUVJUYD50dSeRNitD0pKCHUa3\nJAmWEAG2btUuLBYT230dXxqjn8nEV28Uc9UkzdN/PpmCggn+Dww5E0JO6/D9RPucMm0oYw4bh9Ua\nu2pcbOubwvb9ydVJUwYxe2bLybQ4Ml066eiroh8dZpOu7CbIGCwhAiwpJZotm/LZF4C1xyxFBwbI\nK756s5Kv3oSLfzeLy25ehoH947vCrkGF3y4vckFkMChu/fWJXHHba+2+xhknDef260+Sn+NRbFh6\nD0ZlpLB6Z36HrpMQGcakARkc1y+dHtERhFotFFZU89r8leQUlXZpSYhpg/t02b2ONJJgCRFg0TFh\n1FY7yYxJYmtZx0omuKsaJ2nv/LOcHRtGc/9zi+uWwQm7ukP3EIFRWl7b7nN/ffEkrrhgoiRXx4Co\n0JA2n9M7IYarTshiYEoCRoOB/snxjX5XBqYkMGVQb3w+zYLNO3jokx/YV96O+jBtEB8Ryq+OH92p\n9ziSSYIlRICtX70bgDi3ga0duI4RqMgr87svKs6AMqZC6CUduIMIpMiItr1xKgWjh6Vz9cWTGD4o\nrZOiEt2J1pr8sso2nfPOLRcztGdSq5Nvg0FxwuBMfD648+0vcXrqlnNKjAxjUGoi8zfltDluf1Ji\nIvngt5e2K2E8VkiCJUQAVZTVsGl9LgCVBVXQgdUxEiJCsTcxI21ndjgq7r8oJS9u3UVqj2iio0Ip\nrzjYkmU0Gpg8NpP0tDhiokLJ3VtGTFQoUZE2pk7sT2z00bl8ivDvjrfnsGVvcaPtZqORU0cOICk6\nHINS7CmpID4ilIEpiQxL79Gue504NJO7Z5/AXz76jjG9U7n/vBn8+cO5HX0K9a6fMV6SqxZIgiVE\nAM35ZCU+rw+AqPRoKGn8YtpatdrHXa9fT6jdw48fLiV/eyGFe4oZMWUQV/35fJkx2M2EhVp56fFf\n8f7/VrBzTwkXnzWOQf16EBZqDXZoopv487kzyEyM5eUfluP0eDEaFJdOGsVpowd2yky8yQMymNS/\nF3+/5BS27C3GaDAQEWKlyuHs0HXT46M5e+yQAEXZNmt/2oLJbGTw2Myg3L8tVHdapDErK0uvWLEi\n2GEI0S4et5crznyyfpkc14hYat3eDl1TKfjTNTM57fi6FzOttYzTEeIIV1HroNrhxGQ0dlnphmXb\n99AnIZZLn3mXvDZ2Ux7uj2efyEXHBWe2q9Pu4v/O/idp/ZK4/ekrgv56qJRaqbXO8rdPyjQIESDL\nf9pan1zFJUbg9Pg6fE2tYd22vfj2r70S7BcTIUTHRYWGkBob1aV1scZl9iQ+MozP7ryCP51zYruv\nkxYbyYUThwcwsrax2iz88dXrWL9kG9vX7QlaHK0hCZYQAfLt56uBulan0p42vAFqHf7k+7Vs21MU\nkGsJIY5tIWYTF04cweC09q34MLF/r6B/0ItPjuaB/97Ec/e+z5w3FtKdeuIOFZAESyl1u1JKK6Xi\n9z9WSqmnlVLblFJrlVIyj1Mc1T5//2eWzM+uf1xub/1yKS05dfJg+veS5W+EEIFzVlbbx1CZjAbG\n9O4ey/2k908mKjacZ//vHTavrJsZWVNlJ3fbviBHdlCHB7krpXoCJwO7D9l8CtBv/7/xwHP7/xfi\nqGS1mus/RWkN/WKi2dLBGlgmo4HJo/pw5xXtb84XQgh/Lpk0krjwUL5ek0323mJ2F5cDMH1oXwwK\n5q7bBkBabBS3nTKJEwb3IcRsCnrr1aHComz4fJrfn/YYaX2TqK1yMOm0kdz4t4uCHRoQmFmE/wTu\nAj47ZNts4A1d946zVCkVrZRK1lrvDcD9hOh2ho7qVf9174E9yLG3rYr72CHp9EqOwWwyEh8dTlJc\nBOOGpBMVYQt0qEIIAcDMEf2ZOaI/AHmlFWzMK2T6kL74tGbBphwWZudw26xJRId1v9ehDcu2s3rB\n5vrHedsLsYVbOfG87tOW06EESyk1G8jTWq85LKtNBQ4dfZa7f5skWOKoVLTvYGuVNzGU8uKSNp2f\n3iOGO6+YHuiwhBCiVVJjo0iNjQLAgOLEoZmcOLT7lkJY+f0G3E53/ePpF4zn9qevCGJEjbU4Bksp\nNU8ptd7Pv9nAPcB9HQlAKXWtUmqFUmpFUZEM5BVHpo/eXgLUDXDfVtH2rsEe8RGBDkkIIY46TrsL\ngMvvPpNnv/8jRlNdGnPiueOCGZZfLbZgaa1n+NuulBoG9AYOtF6lAauUUuOAPKDnIYen7d/m7/ov\nAC9AXR2stgQvRHexY2vdwMrI6FB2uT0tHj95ZB9GDUxj2YZdJMVGMHmkLJgqhOgebnrlU+Ijwhjf\ntyeb84vYXlDCH8+ZTnJ0cD8Ibly+nYd//SJ/fPlaHLVOCvaU4PX4OO+mkxg1dVBQY/On3V2EWut1\nQP3UJqXUTiBLa12slPocuFkp9S51g9srZPyVOFpVltdSXFBX/6qirBZLWgwub/M1sFISo7jstCwu\nO81vfTohhAiaaUMy+fOH8/ho2fr6bddWVAc1waquqOWFP31IaUEFvz/9MSadNpLfPXU50QmRjD95\nWNDiak5nLZUzBzgV2AbUAld10n2ECLrsDXmMmZBJr8xE1m/MZZmjoq6vsBmf/bCWwtIqBvRK5Oqz\nJnRRpEII0bKzxw7h75/Px+6qG+PUKz6afsnxQYtHa83LD3xC9uqdAMQlR3Pdg+cTFmFjwszgFT1t\nScASLK11xiFfa+CmQF1biO6susrBlJOGkJ9bhqPaCeaWpzHHRoVx2alZDOuX0gURCiFE6xkNBm6Y\nMZ4n5iyiX484bjtlEqEWc1BiqSip5vk/vs+PHy8HIDEtlgf+ezMJqbFBiactZLFnITpoyozBGE1G\nfnvli+zeWsig4zPYVFLW7Dl3XnGiJFdCiG7ryqlZTB3ch/iIMKJCQ7r8/sV7y/nho2W8/69vqC6v\nBaDfiHTGzxxOz36BXxi7M0iCJUQHPXz3B1RW1JK9MR+AqBYWqlcK+qYndEFkQgjRPgaDIjMpLij3\n/urNRfz7nvfwuA5OGJp27jjufPbKblXotCWSYAnRQZvX51JSVFX/2B5igOrGx/VKjuHmi6aQGBtO\nUqyUZRBCiMO9/rfPePfJrxtsMxgNXHjbrCMquQJJsITokB++XtcguQKw+/yXaXjk1jPokxa8gaJC\nCNGd1VTZ+erNRRiMBnz7Z2KbrSauvGc2vQYkBzm6tpMES4h2Kiup5qcfN5GQFEVRQV1x0bBwK9tK\n/Rcaffeb1fz+VycQEqTBokII0Z2FRdh4d+NjOGqc/PDxcipLqznh7LEkpQenq7Kj1IEFaruDrKws\nvWLFimCHIUSrZW/I42/3fMje3FIABk7ty9JmViSICLVy9VkTuOSUMV0VohBCiE6ilFqptfZb0FBa\nsIRoJ4fdxb23vElVxcGFnQvdzY9wN5uNjOgvsweFEOJoJwmWEO30y/KcBslVWu94NlVUNXn8+SeN\n5JaLpmC1yJ+dOPa89+hnDBibScbQnkQnRAU7HCE6XYuLPQsh/AuxWeq/TukVxwabC28TXe7x0WHc\ndMHxklyJY9Y3r/3A0ze+yNIvVgU7FCG6hLzaC9FOsfHh9V877W50KOBnFvH5J43kV6eNxRYig9vF\nseuVjU8GOwQhupS0YAnRTum9E5h68lAASgorGVxrZkBsdINjEmLCuP1X00iKa33dqwMTT8oKK+hO\nk1CEEEK0niRYQnTAoWOw8neVEKsaNgoXldWwOaegVdfSWvPxk19y1cDbuGXiPfyq9428fv97AY1X\nCCFE15AuQiE64OJfT6G0uIqd2wsB2LxoOz0jbYTYLPTsm0Dm8DQ2bMmnf0YiRkPzn2eUUvQd3Zun\nFj9Ewa4i4lJiiUuO6YqnIYQQIsCkBUuIDhg2uhdX3zKD8y6fRGKPKLSua9WafeF4HJVOPvj3j6xZ\nuJXq2hYWKNxv+JTBRMVH0n9MpiRXQghxBJMES4gOUEoxbnJ/YuPCsYVZmTZrGJNOHETh3nI2rt0D\nQFWNk7++PJeqGkeQoxVCCNFVpItQiA5SSnHuZccx6cRBvPPyAhZ9vwm3q+F6hD+u2May9bs5/fjB\nDOuXwqiBaSTEhDdxRSGEEEc6WSpHiAArL61m6YJsducUM//b9VRW1BIZHcqZl0ygwqaoqnVy5Znj\niIsKC3aoQgghOqC5pXIkwRKiE2mtqaqwY7GaGhQmFUIIceSTtQiFCBKlFJHRocEOQwghRBfr8CB3\npdQtSqnNSqkNSqlHD9n+B6XUNqVUtlJqZkfvI4QQQghxpOhQC5ZSahowGxihtXYqpRL3bx8MXAQM\nAVKAeUqp/lprb0cDFkIIIYTo7jragnUD8IjW2gmgtS7cv3028K7W2qm1zgG2AeM6eC8hhBBCiCNC\nRxOs/sDxSqmflVLzlVJj929PBfYcclzu/m1CCCGEEEe9FrsIlVLzgB5+dt27//xYYAIwFnhfKdWn\nLQEopa4FrgVIT09vy6lCCCGEEN1SiwmW1npGU/uUUjcAH+u6Wg/LlFI+IB7IA3oecmja/m3+rv8C\n8ALUlWlofehCCCGEEN1TR7sIPwWmASil+gMWoBj4HLhIKWVVSvUG+gHLOngvIYQQQogjQkfrYL0C\nvKKUWg+4gCv2t2ZtUEq9D2wEPMBNMoNQCCGEEMeKDiVYWmsXcFkT+x4GHu7I9YU41rkcLiwhUgFe\nCCGONB0uNCqECKzKkirmvbWA60beweV9b2bTz1uDHZIQQog2kqVyhAiyfTsLKckvY8eanZgsJmKS\nogG45dnfMOS4ASilghyhEEKItpIES4gg2b5mJ8/c8jLrF23mr1/dy+nXnyzJlBAiYGoqawmNsMnr\nSpBIgiVEF9u7o4Av/jOXr16aR1VZDUaTkZTMJHkRFEHl9XjZuWEPuzflUZxXitfjJSo+gqReCQw9\nfhAWqznYIQaF0+7k/Uc/Z+W8NYydOYpTfnMisT1iWjzP7XJTU1FL0Z4S8rftY/gJQ4hJjOqCiA/6\n180vkZSewFUPXdyl9xV1JMESoouUFVbw5p/f58sX5uLzaSJiwrjsT+eR3CeJkLCQYIcnjlGVJVU8\ndeOLLP9qNfZqh99j0gel8s8FDxIZF9HF0bXdttU5vPPIJ9z12k1YbdZ2X8ftcvPp01/x6TNfUbi7\nGIANi7N5528fc/r1J3Pe7WcQnxJbf7zX46U4r5TcLfks/nQ53729gNpKe/3+kDAr59x2GsOnDsbn\n0wydPBCbn797p93JD+/+RN6WfCLjIujRO5HwmDAKdhVTU16DxWYhIS2OmKQoCnYVsWtDLntzCqit\nsuPz+vB6vJQVVFC6t4yS/DKMJiO9h6Vz/LkTMJqMDeKtqazF5/WhNXjdHowmI5YQM7YIGwaDDNHu\nKFVXVaF7yMrK0itWrAh2GEIEXHFeCTeMvovyokoAzrhhJlc9dBERMeFBjkwca+zVdn549yeWfbUK\nj8vDxp+yqSqrafE8s8XElPMnMnrGcBLT4xl2/KAGb9haa3au301yZg9CQtuf2DRHa0328m2snb8R\nr8dHSJiV4rxSinKL8XrqkosVX/+C0+4ioWccw44fRHxKLI5aJ9XlNYRHh2EJsVBTUYvH7aHngFSS\n+ySyb2cRXo+XyuIqqsqqie0RzcKPlpK/vaDJWIwmIxGx4RhNBnxeHxVFlfh8rX8/tdos9M/KpPew\ndKITolAGRXFuCQs+WkpVaXUgvl2N7mexWXBUO/B6vM3GqpSiz4hePDH/AUIjbAGP5WiilFqptc7y\nu08SLCE6n8vh4sKUa6kuryFjaE/+88vj8glRdLqygnJWzl3Lim9+YdW8tZjMJqrLa5psqWqL2OQY\nJp89jtS+yVSX17DkfyvYtjoHg9FAYno8Sb0SSEyPJzohEovNwugZwxk+ZXCb73MgcVszfyMLPljC\nuoWbOhy7aJ3RJw1n4hlZDJrQn8wRvTCZpdPrcJJgCdHFqstrUArCosIAWL94M7dPva/+U+PYWSM5\n88ZZjJ4xTOpciSb5fD4+euILcjbsZvwpo5l6wXFNHmuvtrP6u/Us/3o1uVvy2b05n9K9ZV0YbfPC\no8N4dvkjpGT6W9rWv41Lsnn86n+zJzu/EyMTrRESaqXnwBSSM3uQlB5PYnoCqf2TiUuOwefzseij\nn0kfnMZxs8d2WgtmdyQJlhBd6KkbXuCL/8wlNjmGZ37+G2UF5fx+yn047a5Gx0bEhnPTU1cz/dLj\ngxCp6K68Hi+7N+fxzt8+5od3Ftdv7zkwldR+PXA7Pcy8chqTzhpLeVElL9z5Bgs+WEp3ej33574P\n7+D4c8Y3e0xNRQ17dxSyZeUOXrjzDWoqarsoOhEI4dFhjDt1FP3HZBKVEElsj2jSBqSQ2DM+2KF1\nCkmwhOhCl2bcUD8ots/wXlz10MX86cxHmjzebDXz3Mq/02twzyaPEZ2vsqSK+R8sYcvybfQa0pOq\n0mrOuuWU+rpke3cU8MO7i8nbtpeQUCszfjWVtfM3sn1NDuWFlfUJdEiohbiUWCLjIkgflMasq6c1\n2R3s8/ko3VtGwa5idm3MZfnXq9myYjtFe0palSyZzEY87u69CpktPISL7j6bEy48juQ+jWfL5m7d\ny98v/xd5W/firHXicriDFKnoLGarmTOuP5lBE/ox4Yyso6qFSxIsIbpIWUE5l2bciNt58E3ijOtP\nRhkUn//7mybPi4yL4K9z7mHA2L5dEeZRY8faXXjcHjJHZDQYcN0UrTXlhRUU7CqiYFcxOet2sXnZ\nNnKz8ynYVdTo+IiYMM68cRYle8tY+NHSdrWm9ByQQmq/ZHK35FNbacfj9qK1xuf14bK7cLs8bb7m\nkSQk1Mrx503gpMunMurEYfXbtdasmreWp298sdnB5OLoEhkXweDj+pOQVteiZTQaCAmvm02pVF0L\nWGxyDMl9kug7KqNDM0G7QnMJloxYE0eEiuJKnLVOYpNjutVAy5z1u/noiS9YOXcNlSVVGAyGBskV\nwMalW3h22SPs3pTLLz9s8HudypIqvnxhniRYTVj13Tree/RTaivt9B6azomXTKZ0bxmP/OpfaK0Z\nO2skqX2TcTlc5O8ooDi3hPLCSkxmI1mzRpLWL4Xl36wmZ+1uaqvsLd9wv6qyGt5++KMOxb4nO/+Y\nHkPkqHUSkxjVYIC72+XmsauebdD9KY4NlSVVLP3fylYdazIbyRiazqAJ/Tn/jjNI7p3UydEFVvd5\npxKiGd//dxHz3lpAcW4JMT2iiYgJIyIugqi4CKrLa7jo7rPJHJHR9YFpjdvlZuSJQ0lKTyBv+z5W\nzV1LRGw4GUN70iMjkRMuPA6jyUif4RlNJlgAK+euobbKLtOiD1GcV8Jnz3zNh0/8r74rbPPPW/nq\n5e8aHLf8619Yzi9+rzHvzQWdHqdo3oYl2bz5wAdExkZQkl/Kim/XsGPtrmCHJbo5j9vLttU5OGud\nnPu704IdTptJF6FoktY66NXFn7/9dXZvziM0IgSlFPZqBxGx4aT1T2HMScMZMLav3xi9Hi/lRZWE\nR4fWNzFvW53Do1c+w0m/msrg4wYQnRhJVWk1qf2Sm6xH5fV6cVQ76mcDtobb5aaiqJLywkqKckvY\nvSmPVfPWsGreuhbPveWZ33DmjTNbfa+jze7NeWQv20b28m1s+Cmbbatzgh2SECKIMkdm8Oi8+4iM\n7Z5FbqWLULSZz+fjJxfg8AAAEINJREFUp8+Ws2tDLkkZCfQZ3ov0QamYzCZWf7+O9x/7jPDoMBLT\nE4hJimLECUPoN7pPo+s4ap1sWLyZIZMGtmtg4/RLj+eOaX/2263z2p/eZdypo0gfmIbJbKSsoIKi\n3GLytu6jaE8xPp/GZDaS0rcHzloXJfmleNxeXrjrzQbXCY2wMWLaECwhZsKjwohLiSV9UCph0WG8\n9+inbFqyhVEzhuF1e/G4vfQdmcHMq6YRnxpLeVElO9buZtOSbNYv3sze7QX1xUTbw3OUj8dpzpPX\nv8CXL8wNdhhCiCALjw5j6OSBpPbtwWX3nU94dOs/4HYn0oJ1lPP5fOzdUcD2X3aya2MuRpOR2B7R\nOO0uygsrCI0MpUdGAkazEa+7rtWnoqiS0AgbEbHhhMeEERppw+vx4Xa6MRgNGI0GUOCodlBTUYvF\nZmH9ws1sWVk3+ykuNZakXvEU7i4mZ+1uHLVOkvsk8cf3fkd0YhQ71uxi6OSBrf6jKSusIG9LPvnb\nC3j5D29Tuq+8k79rXcdgUGSOzCAuNZZTfj2d484cG+yQgqYot4R7T/srOet2BzsUIUQXM1tMnHnT\nLDJHZjDulFFExUcGO6RWkVmER5Hq/2/vzoOkLu88jr+/zH3f9wHDLQiCICGaGDwRNq66h6sb12s3\nGg+2arcq0ZRbFcvcJlsuajTR1TJsdpOYtVyV1YhiKatCBARkuAYGGJiLGea+YI5+9o9utIHpYQZ6\nppvpz6uqa7p/v1833/nyq2e+/Xue3/O0dlFbWU/B5DxSMpLp6+2jakc1FZsq6Wrv8a4v1dpFa2M7\nh3bXcLD80EnrYYWLqOgoJs4qJj0vjdKZRVyweDpR0VH0dB6joaqRxuomJkwwYhNiychLJ7sok7xJ\nObQeaeOHt/5b2M/3MxwzF03lH356Gxd9bXaoQwkJ5xzVFbVsX7eLd/9zHeX/t3tc/L+KyMgsWj6f\nu35wK1PnlYU6lBFTgTVOdLV18bel99Hd0UNyehIJyfEcrWnWH6Uwl5GXxsTZJUy8oJj8slwKp+Zz\n4WUzz4uFc4NloH+A2sp6qnZWs2/LASo272fPJ/tob+oIdWgiEiLRMVH88M1HuPiqOWc+OExpDNZ5\nrm7/EXZtqKC9uZOswgy69/TQ2dpFZ+uZF2iV4EvLTiEpLRGAuMQ4EpLjmRA1gf6+AQb6+omOjSYu\nMY5ld1/JJcvmj/sFnat2VVO14zBHDjbSVNvM0dpmWo600Xe8z9cV3U5rQxueAU+oQxWRMLLklsvO\n6+LqTFRgjaEf37aS4umFFE8vpKutm86WTlob2ug93s/X772GjPx0ulq7aDjcxKGd1VR8WsnOjyuo\n2VsX6tDFT3JGMktuvpTc0mwmXzSRaRdPHtYkl+NFX28fFZv2s33dTtav3szOj/eEOiQROQ9VV9TR\n0tBGRm5aqEMZFefURWhm84BfAvFAP3C/c+4T8943vxJYDnQDdzrnPj3T542HLsKhpjb4j8f+wKpH\nXx7jiCTYJkRNIKc4i95jvXS39zB3yWweXrUibLr8Du+poXZfPbWVR2iqbSanJJuJs4q58CszRzxJ\na/XeOiq3HuTQrmpq9tVRu6+eyq0HtZyJiATF7Mtm8C+//2eyCjJCPi3Q2Ri1MVhmtgZ4wjn3lpkt\nB77jnFvie74Cb4H1JWClc27oFT45/wus+oMNfPvKR+npPEZ6bhrTFkxmyc2Xcsmy+Z+vRXZ4Tw2b\n13zG+tWb+PSdz0IcsYzUvT+/nT9/4Dpi42JCHcppujt6WPW93/PqU28N2h2XmpXCPT/7O5beecWw\nPu/Ttdt56JrHgh2miMhJzIzsokxKLijigZV3UzqzKNQhDdtojsFywIl7KdOAE+tB3ACsct7qbYOZ\npZtZgXNuXPd15U/K5Rcbf8orT6xm4x+38NGrn/DZBztZeucV3P7ozQCUzCiiZEYRN65YxuE9NbQd\n7aC73Tumqr2pg47mTnp7euk91kd3Rw8Hyw9Rua3qtOVXJDTKP9pN4dR8vnz9wlH5tnW2k7vu23qA\nH39jJYd21QQ8pr2pg6cffIErbrmM2PjYM35mamYyF18z9/PXSWmJpGWlkJKZTGx8LNGx0az77/Xs\n3bx/xPGKSGSad8VsblyxnJi4GKJjosgqyqSgLHdYbdL55lyvYF0AvA0YMAG41DlXZWargZ845z70\nHbcWeMg5d9rlKTO7B7gHoLS0dEFVlZZPOFV/Xz8Hth/izeffZfWvNBFjOLj2ziVc/peLmXzRJFKz\nkoe9IOnAwAD1BxqIjokmKnoCsQmxJCTHExMbQ/mHu3hqxQskpSXy6CvfHrLLsfyj3dTsraP+QAM7\n1+9hy9ryM95NWjglj+kLp/CPz3wzaAPvqytqWXn/82x9rzwonyci49f0hVN4asOPPu/RGQ/O6QqW\nmb0L5A+y6xHgKuCfnHOvmNnNwAvA1SMJzjn3HPAceLsIR/Le8WRgYIDu9h4SUxOIivpiwHRHSye7\n/7SX8g93s2nNthBGKP7WvPQ+a156//PXcQmx5JRkUTyjkNKZxcxcNJWZX5pGdlHmSVekoqKiaKlv\n5YM/rKdi836ioiYQExdNbEIs7Uc76DveT05x1pDFVX9fP+tf28i2D3bQ2tBOX28/KZnJRMdGk1WQ\nTkZ+OtmFmeSX5ZE3KYeSGYUUTy8gIXlkaxx6PB7aGttpONxEW2M7nS2ddLR4r7S2NbbTdrSd2soj\nHNpVPeL8iUhkmTq/jB+88fC4Kq7O5FyvYLUB6c455xvY3uacSzWzXwHvO+d+6ztuD7DkTF2E5/sY\nrOHqauviQPlhyj/czdrfrONoTTNdbd0450hMSWDm4mmYGYd2VtNY3RTqcOUcpGQkMenCUvIm5ZCc\nnkRyehIpGclkFWaQU5JFTkk2mQXpJxXVZ2tgYICu1m48Hg/O43DOEZ8UT0Jy/Gndju3NHWx5dzt7\nNu6js7Ub5/HgcY7eY3001TRz1PdQ17SIBEt2USbX37eUv/nODePmzuvRHOS+C7jPOfe+mV0FPO6c\nW2BmfwY8yBeD3J90zi060+dFSoHlr7+vn9rKI1RsquTxO57WpKERKC4hltJZxUy6sITJcyZSNqeU\nWZfOICEp/rRjnXPeJYi2H6K2sp6GqkbqqxqpqaijZm8dfYOsZWhmJKYmkJaTSm5pNjnFWWx8a8s5\nrZkoInK2ckuzufq2y1m4dB4zFk0Ny5uGhms0C6yv4J2OIRo4hneahs2+q1lPA9fhnabhrsHGX50q\nEgusE17+2Ws8/9BvQh2GhInUrBRu/e5NXPWNr7JpzTZ2bdjLge1VHNh+iK627lCHJyISFDFxMUxb\nMJmZl0xlxqKpTJpdQsGUvEG/YIYjLZUTBAP9A6N2SbOjpZPH/urntDZ4x7W0He3QrNciIhKxMvLS\n+Pq913LjimUkpScGZRjFaFCB5cfj8fDGs2s4vLuGsjmlZBZksPW9chprmrj4qrlMXziZre+V86c3\nP6WlvtU743prF73H+iieXsDcy2cx+7KZzPrydPLLckc8ceNw9HQdY81L77N/20E6WjrpaO6kvamT\nrrZuPAMeBgY83jEzAx76evvDcjFnERGRYDAzkjOSSM1KYe7ls1hw7UV0NHfSUt9Kc30LA/0eCibn\nUTglj8Kp+RROySMpLWmsYlOBdcLrz7zNUw/+e9A+LzUrhYy8NDLy0sidmEPhlHyKpuaTVZhJUloi\nSWmJJKcnkpCSwIQJEzjWfZyW+laa6lpoP9pBTFw0CcnxxCd7ByPHJ8WTmJoQ8PJof18/LUfaaK5r\noamuhU1vb+ONZ98O2u8jIiJyvrtg8TSe/PhHo/7vaLFnP/2DDAI+F+1NHbQ3dVC1c+hb1c2M2PgY\njvf0DutzU7NSyC/LJb8sF8+Ah6PVTTQcbqKlvlUD4UVERIYQDmNVI67AChXn3LCLK/iicKvYVDmK\nUYmIiMhoiJwZv0RERETGiAosERERkSBTgSUiIiISZCqwRERERIIs4ga5l80t5fr7loY6DBERERkl\nmfnpoQ4h8gqs+VfOYf6Vc0IdhoiIiIxj6iIUERERCTIVWCIiIiJBpgJLREREJMhUYImIiIgEmQos\nERERkSBTgSUiIiISZOacC3UMnzOzRqAq1HGEkWzgaKiDCEPKS2DKzeCUl8CUm8EpL4EpN1+Y6JzL\nGWxHWBVYcjIz2+ScWxjqOMKN8hKYcjM45SUw5WZwyktgys3wqItQREREJMhUYImIiIgEmQqs8PZc\nqAMIU8pLYMrN4JSXwJSbwSkvgSk3w6AxWCIiIiJBpitYIiIiIkGmAisMmNlfm9kOM/OY2UK/7ZPM\nrMfMtvoev/Tbt8DMtpvZPjN70swsNNGPrkC58e37ru/332NmS/22X+fbts/MHh77qMeWmT1qZjV+\n58lyv32D5iiSRNr5MBQzO+hrN7aa2Sbftkwze8fM9vp+ZoQ6zrFgZi+aWYOZlfttGzQX5vWk7xz6\nzMwuDl3koytAXtTGnAUVWOGhHPgLYN0g+yqdc/N8j2/5bX8W+CYwzfe4bvTDDIlBc2Nms4BbgNl4\nf/dnzCzKzKKAXwDLgFnArb5jx7sn/M6TNyFwjkIZ5FiL4PNhKFf4zpMTX1geBtY656YBa32vI8FL\nnN5uBsrFMr5oa+/B2/6OVy8x+N8TtTEjpAIrDDjndjnn9gz3eDMrAFKdcxucdxDdKuDGUQswhIbI\nzQ3A75xzx51zB4B9wCLfY59zbr9zrhf4ne/YSBQoR5FE58OZ3QD82vf814zTtuRUzrl1QPMpmwPl\n4gZglfPaAKT72uFxJ0BeAlEbMwQVWOGvzMy2mNkHZvZV37YioNrvmGrftkhSBBz2e30iB4G2j3cP\n+rouXvTr4onUXPhTDk7mgDVmttnM7vFty3PO1fme1wN5oQktLATKhc4jtTEjFh3qACKFmb0L5A+y\n6xHn3GsB3lYHlDrnmsxsAfA/ZjZ71IIMkbPMTUQZKkd4uyu+j/eP5/eBfwXuHrvo5DzyFedcjZnl\nAu+Y2W7/nc45Z2a6tRzl4hRqY86CCqwx4py7+izecxw47nu+2cwqgelADVDsd2ixb9t56Wxyg/f3\nLfF77Z+DQNvPW8PNkZk9D6z2vRwqR5FCOfDjnKvx/Wwws1fxduccMbMC51ydr9urIaRBhlagXET0\neeScO3LiudqY4VMXYRgzs5wTAwbNbDLeAZb7fZew281sse/uwduBSLvS8zpwi5nFmVkZ3tx8AmwE\npplZmZnF4h2A+XoI4xx1p4wFuQnvjQEQOEeRJOLOh0DMLMnMUk48B67Fe668DtzhO+wOIq8t8Rco\nF68Dt/vuJlwMtPl1JY57amPOjq5ghQEzuwl4CsgB/tfMtjrnlgKXA4+ZWR/gAb7lnDsx+PB+vHd7\nJABv+R7jTqDcOOd2mNnLwE6gH3jAOTfge8+DwNtAFPCic25HiMIfK4+b2Ty8l+8PAvcCDJWjSOGc\n64/A8yGQPOBV73cyooH/cs790cw2Ai+b2d8DVcDNIYxxzJjZb4ElQLaZVQPfA37C4Ll4E1iOdxB3\nN3DXmAc8RgLkZYnamJHTTO4iIiIiQaYuQhEREZEgU4ElIiIiEmQqsERERESCTAWWiIiISJCpwBIR\nEREJMhVYIiIiIkGmAktEREQkyFRgiYiIiATZ/wP44yP1N8SRaQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OphHEwouo6Gj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "e1b7524b-fb1d-4585-a357-19dd90b2aa92"
      },
      "source": [
        "# Create a legend\n",
        "fig, ax = plt.subplots(1,1)\n",
        "southern_hemisphere.plot(column='gdp_md_est', ax=ax, legend=True)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fee857e7630>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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T+P5jr7C37CBxMV427ml7f6tLT5rCV2ZNiUj7j5mmdQgGsIDQq6kqS1/6mL6D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\nYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rAbw9otwpmZd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Tutorial: http://geopandas.org/mapping.html"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}